Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f714075de80>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f7140690cc0>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """

    input_images = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='input_images')
    z_input = tf.placeholder(tf.float32, [None, z_dim], name='z_input')
    l_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return input_images, z_input, l_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False, alpha=0.2, keep_prob=0.8):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        #image is 28x28x3        
        
        #Tried first without the xavier initialization. It seems to perform OK without it
        #Added Xavier initialization since in my other GAN exercises leaving the default 
        #caused erratic runs and difficulty when converging
        
        #First Convolution
        conv1 = tf.layers.conv2d(images, 128, 5, 2, padding='same', 
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * conv1, conv1)
        #14x14x64
        
        #Second Convolution
        conv2 = tf.layers.conv2d(relu1, 256, 5, 2, padding='same', 
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        batch2 = tf.layers.batch_normalization(conv2, training=True)
        relu2 = tf.maximum(alpha * batch2, batch2)
        #7x7x128
        
        #Third Convolution
        conv3 = tf.layers.conv2d(relu2, 512, 5, 2, padding='same', 
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        batch3 = tf.layers.batch_normalization(conv3, training=True)
        relu3 = tf.maximum(alpha * batch3, batch3)   
        drop3 = tf.nn.dropout(relu3, keep_prob)
        #4x4x256
        
        flat = tf.reshape(drop3, (-1, 4*4*512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True, alpha=0.2, keep_prob=0.8):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        dense = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start convolutionals
        conv1 = tf.reshape(dense, (-1, 7, 7, 512))
        batch1 = tf.layers.batch_normalization(conv1, training=is_train)
        relu1 = tf.maximum(alpha * batch1, batch1)
        drop1 = tf.nn.dropout(relu1, keep_prob)
        # 7x7x512
        
        #Second Conv
        conv2 = tf.layers.conv2d_transpose(drop1, 256, 5, strides=2, padding='same', 
                                           kernel_initializer=tf.contrib.layers.xavier_initializer())
        batch2 = tf.layers.batch_normalization(conv2, training=is_train)
        relu2 = tf.maximum(alpha * batch2, batch2)
        # 14x14x256
        
        #Third Conv
        conv3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same', 
                                           kernel_initializer=tf.contrib.layers.xavier_initializer())
        batch3 = tf.layers.batch_normalization(conv3, training=is_train)
        relu3 = tf.maximum(alpha * batch3, batch3)
        # 28x28x256
        
        # Output layer
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=1, padding='same', 
                                            kernel_initializer=tf.contrib.layers.xavier_initializer())
        # 28x28x3
        
        output = tf.tanh(logits)   
    
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim, alpha=0.2, smooth=0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1.-smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
     
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=25, show_every=100):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    #saver = tf.train.Saver()
    
    #print(data_shape)
    img_w = data_shape[1]
    img_h = data_shape[2]
    img_channels = data_shape[3]
      
    input_real, input_z, l_rate = model_inputs(img_w, img_h, img_channels, z_dim)      
    d_loss, g_loss = model_loss(input_real, input_z, img_channels, alpha=0.02)        
    d_opt, g_opt = model_opt(d_loss, g_loss, l_rate, beta1)
    
    loss_d, loss_g = [], []
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_images *= 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)).astype(np.float32)
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, l_rate:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, l_rate:learning_rate})
                # Second run of Generator to prevent discriminator loss from going to zero.
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, l_rate:learning_rate})
                
                if steps % print_every == 0:                    
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{} ".format(epoch_i+1, epoch_count), 
                          "Batch {} ".format(steps),
                          "Discriminator Loss: {:.4f} ".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    loss_d.append(train_loss_d)
                    loss_g.append(train_loss_g)
                    
                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, img_channels, data_image_mode)
            
            #saver.save(sess, './checkpoints/generator.ckpt')        
        return loss_d, loss_g

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.0004
beta1 = 0.5 #Same as the DCGAN paper


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2  Batch 25  Discriminator Loss: 1.1783  Generator Loss: 0.9493
Epoch 1/2  Batch 50  Discriminator Loss: 1.8785  Generator Loss: 0.6544
Epoch 1/2  Batch 75  Discriminator Loss: 1.8456  Generator Loss: 0.3360
Epoch 1/2  Batch 100  Discriminator Loss: 1.8577  Generator Loss: 0.3352

Epoch 1/2  Batch 125  Discriminator Loss: 2.0319  Generator Loss: 0.2707
Epoch 1/2  Batch 150  Discriminator Loss: 1.5954  Generator Loss: 0.5507
Epoch 1/2  Batch 175  Discriminator Loss: 1.7067  Generator Loss: 0.4077
Epoch 1/2  Batch 200  Discriminator Loss: 1.5669  Generator Loss: 0.8342

Epoch 1/2  Batch 225  Discriminator Loss: 1.4289  Generator Loss: 0.9730
Epoch 1/2  Batch 250  Discriminator Loss: 1.5947  Generator Loss: 0.5036
Epoch 1/2  Batch 275  Discriminator Loss: 1.4702  Generator Loss: 0.7563
Epoch 1/2  Batch 300  Discriminator Loss: 1.5499  Generator Loss: 0.5834

Epoch 1/2  Batch 325  Discriminator Loss: 1.4484  Generator Loss: 1.0946
Epoch 1/2  Batch 350  Discriminator Loss: 1.3793  Generator Loss: 1.0136
Epoch 1/2  Batch 375  Discriminator Loss: 1.4150  Generator Loss: 0.7176
Epoch 1/2  Batch 400  Discriminator Loss: 1.5628  Generator Loss: 0.4614

Epoch 1/2  Batch 425  Discriminator Loss: 1.5626  Generator Loss: 1.0900
Epoch 1/2  Batch 450  Discriminator Loss: 1.3981  Generator Loss: 0.5962
Epoch 1/2  Batch 475  Discriminator Loss: 1.5902  Generator Loss: 0.5037
Epoch 1/2  Batch 500  Discriminator Loss: 1.6889  Generator Loss: 0.3766

Epoch 1/2  Batch 525  Discriminator Loss: 1.9038  Generator Loss: 1.6004
Epoch 1/2  Batch 550  Discriminator Loss: 1.4752  Generator Loss: 0.4851
Epoch 1/2  Batch 575  Discriminator Loss: 1.4180  Generator Loss: 0.9506
Epoch 1/2  Batch 600  Discriminator Loss: 1.8425  Generator Loss: 0.3358

Epoch 1/2  Batch 625  Discriminator Loss: 1.4354  Generator Loss: 0.6132
Epoch 1/2  Batch 650  Discriminator Loss: 2.1498  Generator Loss: 0.2133
Epoch 1/2  Batch 675  Discriminator Loss: 1.4952  Generator Loss: 0.7618
Epoch 1/2  Batch 700  Discriminator Loss: 1.4508  Generator Loss: 0.8239

Epoch 1/2  Batch 725  Discriminator Loss: 1.4752  Generator Loss: 0.5755
Epoch 1/2  Batch 750  Discriminator Loss: 1.4217  Generator Loss: 0.6405
Epoch 1/2  Batch 775  Discriminator Loss: 1.4094  Generator Loss: 0.7945
Epoch 1/2  Batch 800  Discriminator Loss: 1.6547  Generator Loss: 0.4039

Epoch 1/2  Batch 825  Discriminator Loss: 1.4513  Generator Loss: 0.5455
Epoch 1/2  Batch 850  Discriminator Loss: 1.4262  Generator Loss: 0.6095
Epoch 1/2  Batch 875  Discriminator Loss: 1.4082  Generator Loss: 0.6649
Epoch 1/2  Batch 900  Discriminator Loss: 1.3570  Generator Loss: 0.6989

Epoch 1/2  Batch 925  Discriminator Loss: 1.4380  Generator Loss: 0.7426
Epoch 2/2  Batch 25  Discriminator Loss: 1.2591  Generator Loss: 0.9078
Epoch 2/2  Batch 50  Discriminator Loss: 1.2991  Generator Loss: 1.6629
Epoch 2/2  Batch 75  Discriminator Loss: 1.3594  Generator Loss: 0.9390
Epoch 2/2  Batch 100  Discriminator Loss: 1.6044  Generator Loss: 0.3707

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/2  Batch 25  Discriminator Loss: 0.7693  Generator Loss: 1.6066
Epoch 1/2  Batch 50  Discriminator Loss: 1.2596  Generator Loss: 0.7912
Epoch 1/2  Batch 75  Discriminator Loss: 1.5807  Generator Loss: 0.7013
Epoch 1/2  Batch 100  Discriminator Loss: 1.5597  Generator Loss: 0.5037

Epoch 1/2  Batch 125  Discriminator Loss: 0.9531  Generator Loss: 1.1105
Epoch 1/2  Batch 150  Discriminator Loss: 1.2003  Generator Loss: 1.2346
Epoch 1/2  Batch 175  Discriminator Loss: 1.3878  Generator Loss: 0.8535
Epoch 1/2  Batch 200  Discriminator Loss: 0.6566  Generator Loss: 2.5746

Epoch 1/2  Batch 225  Discriminator Loss: 0.8168  Generator Loss: 1.2033
Epoch 1/2  Batch 250  Discriminator Loss: 1.6213  Generator Loss: 0.5293
Epoch 1/2  Batch 275  Discriminator Loss: 1.0983  Generator Loss: 1.2934
Epoch 1/2  Batch 300  Discriminator Loss: 0.9690  Generator Loss: 2.9197

Epoch 1/2  Batch 325  Discriminator Loss: 1.0266  Generator Loss: 1.2698
Epoch 1/2  Batch 350  Discriminator Loss: 1.2771  Generator Loss: 1.1804
Epoch 1/2  Batch 375  Discriminator Loss: 1.4136  Generator Loss: 0.5970
Epoch 1/2  Batch 400  Discriminator Loss: 1.5183  Generator Loss: 0.6356

Epoch 1/2  Batch 425  Discriminator Loss: 0.9864  Generator Loss: 1.2608
Epoch 1/2  Batch 450  Discriminator Loss: 2.0022  Generator Loss: 0.2813
Epoch 1/2  Batch 475  Discriminator Loss: 1.6025  Generator Loss: 0.5791
Epoch 1/2  Batch 500  Discriminator Loss: 1.2004  Generator Loss: 0.8816

Epoch 1/2  Batch 525  Discriminator Loss: 1.3010  Generator Loss: 0.7692
Epoch 1/2  Batch 550  Discriminator Loss: 1.1698  Generator Loss: 1.4527
Epoch 1/2  Batch 575  Discriminator Loss: 1.1169  Generator Loss: 1.7353
Epoch 1/2  Batch 600  Discriminator Loss: 1.1673  Generator Loss: 0.7306

Epoch 1/2  Batch 625  Discriminator Loss: 0.7788  Generator Loss: 2.5772
Epoch 1/2  Batch 650  Discriminator Loss: 1.3211  Generator Loss: 0.7250
Epoch 1/2  Batch 675  Discriminator Loss: 1.0843  Generator Loss: 1.0982
Epoch 1/2  Batch 700  Discriminator Loss: 1.4865  Generator Loss: 1.2394

Epoch 1/2  Batch 725  Discriminator Loss: 0.7721  Generator Loss: 4.2690
Epoch 1/2  Batch 750  Discriminator Loss: 0.8113  Generator Loss: 1.4982
Epoch 1/2  Batch 775  Discriminator Loss: 1.1991  Generator Loss: 1.0337
Epoch 1/2  Batch 800  Discriminator Loss: 0.9964  Generator Loss: 1.7935

Epoch 1/2  Batch 825  Discriminator Loss: 1.4866  Generator Loss: 0.4558
Epoch 1/2  Batch 850  Discriminator Loss: 1.0189  Generator Loss: 1.4189
Epoch 1/2  Batch 875  Discriminator Loss: 1.4684  Generator Loss: 0.4721
Epoch 1/2  Batch 900  Discriminator Loss: 1.3565  Generator Loss: 0.6761

Epoch 1/2  Batch 925  Discriminator Loss: 0.8380  Generator Loss: 1.4156
Epoch 1/2  Batch 950  Discriminator Loss: 1.7348  Generator Loss: 0.3833
Epoch 1/2  Batch 975  Discriminator Loss: 1.0299  Generator Loss: 2.4389
Epoch 1/2  Batch 1000  Discriminator Loss: 1.1280  Generator Loss: 0.8660

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.